CNOP‐Based Adaptive Observation Network Designed for Improving Upstream Kuroshio Transport Prediction

Abstract

Abstract Using the Regional Ocean Modeling System and the conditional nonlinear optimal perturbation (CNOP) approach, this study explored a CNOP‐based adaptive observation system design with regard to improving the prediction of one seasonal upstream Kuroshio transport (UKT) reduction event. For comparison, investigations were also conducted using a set of random observation systems built within a sensitive area inferred from previous studies and a conventional observation system built at 18°N. For these observation systems, observation networks constructed using different numbers of observation sites and observation distances (i.e., the shortest distances between any two observation sites) were evaluated by performing observing system simulation experiments. Results showed that most assimilation experiments can improve the UKT prediction, indicating the importance of initial conditions. For all observation systems, large numbers of observation sites generally led to higher prediction benefits, whereas the effects of observation distances differed significantly. Overall, the CNOP‐based observation system exhibited the best performance. Optimal CNOP‐based observation networks were established using six or eight observation sites and observation distances of 140 or 165 km, which produced a mean prediction improvement of 43.1%. This preliminary work is expected to provide guidance on future observations in the upstream Kuroshio region and to help realistic UKT predictions.